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Efficient Multidimensional Fuzzy Search for Personal Information Management Systems

机译:个人信息管理系统的高效多维模糊搜索

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摘要

With the explosion in the amount of semistructured data users access and store in personal information management systems, there is a critical need for powerful search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically support some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multidimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in noncontent dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.
机译:随着用户访问和存储在个人信息管理系统中的半结构化数据数量的激增,迫切需要功能强大的搜索工具,以一种简单有效的方式来检索通常非常异构的数据。现有工具通常在查询的文本部分上支持某些IR样式的排名,但仅将结构(例如文件目录)和元数据(例如日期,文件类型)视为过滤条件。我们提出了一种新颖的多维搜索方法,该方法允许用户除关键字条件之外还对结构和元数据条件执行模糊搜索。我们的技术分别对每个维度评分,并将三个维度的评分整合为有意义的统一评分。我们还设计索引和算法,以有效地识别与多维查询最相关的文件。我们对方法进行了全面的实验评估,结果表明我们针对非内容维度中的模糊查询条件的松弛和评分框架可以显着提高排名准确性。我们还表明,我们的查询处理策略可以很好地执行和扩展,使我们的模糊搜索方法适合日常使用。

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